The study introduces an advanced workflow for comprehensive proteomic analysis using micropillar array columns (μPACs), wide-window acquisition (WWA), and AI-based data analysis. This approach significantly improves proteomic coverage, throughput, and precision. μPACs, with their unique pillar array design, reduce peak broadening and enhance separation power, leading to more than 50% more peptides and 24% more proteins identified compared to packed bed columns. WWA, combined with the AI-driven CHIMERS search engine, further boosts identification rates by allowing the confident identification of peptides from chimeric spectra. The optimized platform demonstrated excellent performance in various experiments, including bulk proteomics, affinity purification mass spectrometry, and single-cell proteomics. It achieved a 92% increase in potential interactors in a study on the chromatin remodeler Smarca5/Snf2h, identifying both known and novel binding partners. The workflow also showed high precision and accuracy, with coefficients of variation (CVs) <7% for low-input bulk samples and deviations <10% from expected fold changes for regular abundance two-proteome mixes. Overall, the combination of μPACs, WWA, and AI-driven data analysis represents a significant advancement in proteomic analysis, offering improved sensitivity, throughput, and proteome coverage.The study introduces an advanced workflow for comprehensive proteomic analysis using micropillar array columns (μPACs), wide-window acquisition (WWA), and AI-based data analysis. This approach significantly improves proteomic coverage, throughput, and precision. μPACs, with their unique pillar array design, reduce peak broadening and enhance separation power, leading to more than 50% more peptides and 24% more proteins identified compared to packed bed columns. WWA, combined with the AI-driven CHIMERS search engine, further boosts identification rates by allowing the confident identification of peptides from chimeric spectra. The optimized platform demonstrated excellent performance in various experiments, including bulk proteomics, affinity purification mass spectrometry, and single-cell proteomics. It achieved a 92% increase in potential interactors in a study on the chromatin remodeler Smarca5/Snf2h, identifying both known and novel binding partners. The workflow also showed high precision and accuracy, with coefficients of variation (CVs) <7% for low-input bulk samples and deviations <10% from expected fold changes for regular abundance two-proteome mixes. Overall, the combination of μPACs, WWA, and AI-driven data analysis represents a significant advancement in proteomic analysis, offering improved sensitivity, throughput, and proteome coverage.